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A Brief Bibliography of Interestingness Measure, Bayesian Belief Network and Causal Inference Papers by Adnan Masood Doctoral Student http://scis.nova.edu/~adnan Graduate School of Computer and Information Sciences Nova Southeastern University 2012
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3Choi, A., & Darwiche, A. (2011). Relax, compensate and then recover. New Frontiers in Artiﬁcial Intelli- gence, 167–180.Cooper, G. (1988). A method for using belief networks as inﬂuence diagrams. In Workshop on uncertainty in artiﬁcial intelligence (pp. 55–63).Cooper, G. (1990). Probabilistic inference using belief networks is np-hard. Artiﬁcial Intelligence, 42(1), 393-405.Cooper, G., & Herskovits, E. (1992). A bayesian method for the induction of probabilistic networks from data. Machine learning, 9(4), 309-347.Coupe, V., & Van Der Gaag, L. (1998). Practicable sensitivity analysis of bayesian belief networks. UU- CS(1998-10).Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel- based learning methods. Cambridge university press.De Finetti, B. (1961). The bayesian approach to the rejection of outliers. In Proceedings of the fourth berkeley symposium on mathematical statistics and probability, volume 1: Contributions to the theory of statistics (Vol. 1, pp. 199–210). Univ of California Press.Dittmer, S., & Jensen, F. (1997). Tools for explanation in bayesian networks with application to an agricultural problem. In Proceedings of the ﬁrst european conference for information technology in agriculture (pp. 15–18).Druzdel, M., & Van Der Gaag, L. (2000). Building probabilistic networks:" where do the numbers come from?". IEEE Transactions on knowledge and data engineering, 12(4), 481–486.Druzdzel, M. (1996). Qualitiative verbal explanations in bayesian belief networks. AISB QUARTERLY, 43–54.Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.Ferreira, P., Alves, R., Belo, O., & Cortes, L. (2006). Establishing fraud detection patterns based on signa- tures. Advances in Data Mining, 526–538.Fitelson, B., & Hitchcock, C. (2010). Probabilistic measures of causal strength. Causality in the Sciences.Francisci, D., & Collard, M. (2003). Multi-criteria evaluation of interesting dependencies according to a data mining approach. In In congress on evolutionary computation (Vol. 3, p. 1568-1574). IEEE Press.Frank, A., & Asuncion, A. (2010). UCI machine learning repository. Available from http://archive.ics.uci.edu/mlFriedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classiﬁers. Machine learning, 29(2), 131–163.Garriga, J. (2009). The right will for seeing and believing. UMI Proquest.Garriga, J. (2011). Groundwork for a new approach to knowledge discovery.Geng, L., & Hamilton, H. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys (CSUR), 38(3), 9.Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009, November). The weka data mining software: an update. SIGKDD Explor. Newsl., 11(1), 10–18. Available from http://doi.acm.org/10.1145/1656274.1656278Han, J., & Kamber, M. (2006). Data mining: concepts and techniques. Morgan Kaufmann.Hatonen, K., Klemettinen, M., Mannila, H., Ronkainen, P., & Toivonen, H. (1996). Knowledge discov- ery from telecommunication network alarm databases. In Proceedings of the twelfth international conference on data engineering, 1996. (pp. 115–122).Hawkins, D. M. (1980). Identiﬁcation of outliers. Chapman & Hall.Heckerman, D. (2008). A tutorial on learning with bayesian networks. Innovations in Bayesian Networks, 33-82.Henrion, M., & Cooley, D. R. (1987). An experimental comparison of knowledge engineer- ing for expert systems and for decision analysis. In Proceedings of the sixth national con- ference on artiﬁcial intelligence - volume 2 (pp. 471–476). AAAI Press. Available from http://dl.acm.org/citation.cfm?id=1863766.1863781
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4Hilderman, R., & Hamilton, H. (2001). Knowledge discovery and measures of interest. Springer Netherlands.Hornik, K., Leisch, F., & Zeileis, A. (2003, March). Learning bayesian networks with r. In K. Hornik, F. Leisch, & A. Zeileis (Eds.), Proceedings of the 3rd international workshop on distributed statistical computing (dsc 2003) (p. 2). Vienna, Austria. Available from http://www.r-project.org/conferences/DSC-2003/Proceedings/BottcherDethlefsen.pdfHoward, R., & Matheson, J. (2005). Inﬂuence diagrams. Decision Analysis, 2(3), 127–143.Huang, C., Chen, M., & Wang, C. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications, 33(4), 847–856.Huelsenbeck, J., & Ronquist, F. (2001). Mrbayes: Bayesian inference of phylogenetic trees. Bioinformatics, 17(8), 754-755.Imberman, S., Domanski, B., & Thompson, H. (2001). Boolean analyzer-an algorithm that uses a probabilis- tic interestingness measure to ﬁnd dependency/association rules in a head trauma data.Jaroszewicz, S., & Scheffer, T. (2005). Fast discovery of unexpected patterns in data, relative to a bayesian network. In Proceedings of the eleventh acm sigkdd international conference on knowledge discovery in data mining (pp. 118–127).Jaroszewicz, S., Scheffer, T., & Simovici, D. (2009). Scalable pattern mining with bayesian networks as background knowledge. Data Mining and Knowledge Discovery, 18(1), 56–100.Jaroszewicz, S., & Simovici, D. (2004). Interestingness of frequent itemsets using bayesian networks as background knowledge. In Proceedings of the tenth acm sigkdd international conference on knowledge discovery and data mining (pp. 178–186).Jensen, F. (1997). Bayesian networks and inﬂuence diagrams. Risk Management Strategies in Agriculture, 199–213.Joshi, M. V., Agarwal, R. C., & Kumar, V. (2001). Mining needle in a haystack: classifying rare classes via two-phase rule induction. In Proceedings of the 2001 acm sigmod international con- ference on management of data (pp. 91–102). New York, NY, USA: ACM. Available from http://doi.acm.org/10.1145/375663.375673Kaur, H. (2005). Actionable rules: issues and new directions. In Proceedings of world academy of science, engineering and technology (pp. 61–64). World Academy of Science, Engineering and Technology.Khairuddin, M., Zhang, P., & Rao, A. (2008). Risk mitigation strategies for the prepaid card issuer in aus- tralia. Security Research Centre, School of Computer and Security Science, Edith Cowan University, Perth, Western Australia.Khashman, A. (2010, September). Neural networks for credit risk evaluation: Investigation of differ- ent neural models and learning schemes. Expert Syst. Appl., 37(9), 6233–6239. Available from http://dx.doi.org/10.1016/j.eswa.2010.02.101Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent ﬁnancial statements. Expert Systems with Applications, 32(4), 995-1003.Knorr, E. M., & Ng, R. T. (1998). Algorithms for mining distance-based outliers in large datasets. In Proceedings of the 24rd international conference on very large data bases (pp. 392–403). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. Available from http://dl.acm.org/citation.cfm?id=645924.671334Koller, D., & Friedman, N. (2009). Probabilistic graphical models. MIT press.Korb, K. B., & Nicholson, A. E. (2010). Bayesian artiﬁcial intelligence, second edition (2nd ed.). Boca Raton, FL, USA: CRC Press, Inc.Koski, T., & Noble, J. (2009). Bayesian networks: An introduction (1st ed.). Wiley Publishing.Krieg, M. (2001). A tutorial on bayesian belief networks.Lacave, C., & Díez, F. (2002). A review of explanation methods for bayesian networks. The Knowledge Engineering Review, 17(2), 107–127.Lallich, S., Vaillant, B., & Lenca, P. (2005). Parametrised measures for the evaluation of as- sociation rule interestingness. In Proceedings of the 6th international symposium on ap-
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5 plied stochastic models and data analysis (asmda 2005) (pp. 220–229). Available from http://asmda2005.enst-bretagne.fr/IMG/pdf/proceedings/220.pdfLaskey, K. (1995). Sensitivity analysis for probability assessments in bayesian networks. IEEE Transactions on Systems, Man and Cybernetics, 25(6), 901–909.Lawrence, N. (2000). Variational inference in probabilistic models. Unpublished doctoral dissertation, University of Cambridge, UK.Lazarevic, A., Banerjee, A., Chandola, V., Kumar, V., & Srivastava, J. (2008). Data mining for anomaly detection. In Tutorial at the european conference on principles and practice of knowledge discovery in databases, antwerp, belgium, september (Vol. 19).Lazarevic, A., & Kumar, V. (2005). Feature bagging for outlier detection. In Proceedings of the eleventh acm sigkdd international conference on knowledge discovery in data mining (pp. 157–166). New York, NY, USA: ACM. Available from http://doi.acm.org/10.1145/1081870.1081891Loh, W.-Y. (2008). Classiﬁcation and regression tree methods. In Encyclopedia of statistics in quality and reliability. John Wiley & Sons, Ltd. Available from http://dx.doi.org/10.1002/9780470061572.eqr492Lyons, L. (2007, November). A particle physicist’s perspective on astrostatistics. In G. J. Babu & E. D. Feigel- son (Eds.), Statistical challenges in modern astronomy iv (Vol. 371, p. 361).Malhas, R., & Aghbari, Z. (2009). Interestingness ﬁltering engine: Mining bayesian networks for interesting patterns. Expert Systems with Applications, 36(3), 5137–5145.Malhas, R., & Al Aghbari, Z. (2006). An efﬁcient bayesian network approach for discovering interesting patterns. Data Mining VII: Data, Text, and Web Mining and Their Business Applications, 103.Malhas, R., & Al Aghbari, Z. (2007). Fast discovery of interesting patterns based on bayesian network background knowledge. University of Sharjah Journal of Pure & Applied Sciences, 4(3), 29–47.Malhas, R., & Al Aghbari, Z. (2008). Using sensitivity of a bayesian network to discover interest- ing patterns. In Proceedings of the 2008 ieee/acs international conference on computer systems and applications (pp. 196–205). Washington, DC, USA: IEEE Computer Society. Available from http://dx.doi.org/10.1109/AICCSA.2008.4493535Meek, C. (1995). Causal inference and causal explanation with background knowledge. In Uncertainty in artiﬁcial intelligence (Vol. 11, pp. 403–410). Morgan Kaufmann Publishers, Inc., San Mateo, CA.MejiaÂa-Lavalle, M., & Sanchez Vivar, A. (2009). Outlier detection with explanation facility. Machine Learning and Data Mining in Pattern Recognition, 454-464.Mejia-Lavalle, M. (2010). Outlier detection with innovative explanation facility over a very large ﬁnancial database. In Electronics, robotics and automotive mechanics conference (cerma), 2010 (pp. 23–27).Mejia-Lavalle, M., M.a-Lavalle, Obregon, R., & Vivar, A. (2009). Outlier detection with a hybrid artiﬁcial intelligence method. MICAI 2009: Advances in Artiﬁcial Intelligence, 590-599.Minka, T., Winn, J., Guiver, J., & Kannan, A. (2009). Infer .net 2.3, 2009. microsoft research cambridge.Moole, B., & Valtorta, M. (2004). Sequential and parallel algorithms for causal explanation with background knowledge. International journal of uncertainty fuzziness and knowledge based systems, 12, 101–122.Murphy, K. (2007). Software packages for graphical models/bayesian networks. Bulletin International Society of Bayesian Analysis, 14.Neapolitan, R. (2004). Learning bayesian networks. Pearson Prentice Hall Upper Saddle River, NJ.Neyman, J. (1977). Frequentist probability and frequentist statistics. Synthese, 36(1), 97-131.Padmaja, T. M., Dhulipalla, N., Bapi, R. S., & Krishna, P. R. (2007). Unbalanced data classiﬁcation using extreme outlier elimination and sampling techniques for fraud detection. , 511–516. Available from http://dx.doi.org/10.1109/ADCOM.2007.131Padmanabhan, B., & Tuzhilin, A. (1998). A belief-driven method for discovering unexpected patterns. In Proceedings of the fourth international conference on knowledge discovery and data mining (Vol. 1, pp. 94–100).Padmanabhan, B., & Tuzhilin, A. (1999). Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems, 27(3), 303–318.Padmanabhan, B., & Tuzhilin, A. (2000). Small is beautiful: discovering the minimal set of un-
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7Van Allen, T., Singh, A., Greiner, R., & Hooper, P. (2008). Quantifying the uncertainty of a belief net response: Bayesian error-bars for belief net inference. Artiﬁcial Intelligence, 172(4-5), 483–513.Van Koten, C., & Gray, A. (2006). An application of bayesian network for predicting object-oriented software maintainability. Information and Software Technology, 48(1), 59-67.Witten, I., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann Pub.Wooldridge, S. (2003). Bayesian belief networks. CISRO 2003.Youn, S., & McLeod, D. (2007). A comparative study for email classiﬁcation. Advances and Innovations in Systems, Computing Sciences and Software Engineering, 387–391.Zhang, Y., Meratnia, N., & Havinga, P. (2010). Outlier detection techniques for wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 12(2), 159–170.
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